Optimal warning distance

ABSTRACT

Systems, methods, and apparatuses are provided for determining an optimal warning distance for a vehicle. For example, the geographic location of the vehicle is received along with a reaction profile of the operator of the vehicle. Based on the geographic location, a roadway condition is determined. An optimal warning distance is then determined based on a braking distance of the vehicle and the reaction profile of the operator of the vehicle. The operator of the vehicle, or a navigation system of the vehicle itself, is alerted to the roadway condition when the vehicle is located at the optimal warning distance.

CROSS REFERENCE TO RELATED APPLICATION

This application is a divisional under 35 U.S.C. § 121 and 37 C.F.R. §1.53(b) of U.S. patent application Ser. No. 15/352,251 filed Nov. 15,2016, now U.S. Pat. No. 10,043,393, which is a continuation of U.S.patent application Ser. No. 14/462,841 filed Aug. 19, 2014 now U.S. Pat.No. 9,514,651 granted Dec. 6, 2016, each of which is incorporated hereinby reference in its entirety.

FIELD

The following disclosure relates to systems, methods, and apparatusesfor determining an optimal warning distance for a vehicle operator orvehicle navigation system.

BACKGROUND

As a driver of a vehicle travels along a roadway, the driver may have toreduce the vehicle speed due to an upcoming curvature in the road,school or park zone, weather condition (e.g., black ice), trafficincident, or traffic congestion. There may be a desire to alert thedriver of the upcoming roadway condition. If the warning or alertmessage is generated too close to the event, the driver may not haveenough time to stop or slow down. On the other hand, if the warning oralert message is generated too early; the driver may react unnecessarilyand affect neighboring vehicles. Therefore, there is a continuing effortto provide improved systems, methods, and apparatuses that determine anoptimal warning distance for a driver or vehicle operator.

SUMMARY

Systems, methods, and apparatuses are provided for determining anoptimal warning distance for an individual vehicle operator or vehiclenavigation system. In one embodiment, the method comprises receiving ageographic location of a vehicle. The method further comprises receivinga reaction profile of an operator of the vehicle. The method furthercomprises determining a roadway condition based on the geographiclocation of the vehicle. The method further comprises calculating anoptimal warning distance from the roadway condition based on a brakingdistance of the vehicle and the reaction profile of the operator. Themethod further comprises alerting the operator or a navigation system ofthe vehicle to the roadway condition when the vehicle is located at theoptimal warning distance.

In another embodiment, a non-transient computer readable mediumcontaining program instructions for causing a computer to perform themethod of: (1) receiving a geographic location and direction of travelof an autonomous vehicle; (2) receiving a reaction profile of anoperator of the vehicle selected from the group consisting of: anoperator's age, an operator's health, an operator's preferred drivingstyle, and combinations thereof; (3) determining a roadway condition inthe direction of travel of the vehicle based on the geographic locationof the vehicle; (4) calculating an optimal warning distance from theroadway condition based on a braking distance of the vehicle and thereaction profile of the operator; and (5) alerting a navigation systemof the autonomous vehicle to the roadway condition when the vehicle islocated at the optimal warning distance.

Apparatuses are also provided for determining optimal warning distances.In one embodiment, a device comprises at least one processor and atleast one memory including computer program code for one or moreprograms, wherein the memory and the computer program code configuredto, with the processor, cause the device to at least perform: (1)receive a geographic location and direction of travel of a vehicle; (2)receive a reaction profile of an operator of the vehicle; (3) determinea roadway condition in the direction of travel of the vehicle based onthe geographic location of the vehicle; (4) calculate an optimal warningdistance from the roadway condition based on a braking distance of thevehicle and the reaction profile of the operator; and (5) alert theoperator or a navigation system of the vehicle to the roadway conditionwhen the vehicle is located at the optimal warning distance.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments are described herein with reference to thefollowing drawings.

FIG. 1 illustrates an example scenario for calculating an optimalwarning distance.

FIG. 2 illustrates an example flowchart for determining an optimalwarning distance for a vehicle operator or vehicle navigation system.

FIG. 3 illustrates an example system for determining an optimal warningdistance for a vehicle operator or vehicle navigation system.

FIG. 4 illustrates an exemplary navigation device of the system of FIG.3.

FIG. 5 illustrates an exemplary server of the system of FIG. 3.

DETAILED DESCRIPTION

The optimal warning distance for alerting an individual vehicle operatoror vehicle navigation system of an upcoming roadway condition (e.g.,curve in the road, inclement weather, traffic accident) may vary basedon the identified operator. For example, one vehicle operator may have adifferent reaction time required to safely adjust the speed of thevehicle than another vehicle operator for the upcoming roadwaycondition. Additionally, an individual vehicle operator may individuallyhave different reaction times for a specific roadway condition basedupon the time of the day, day of the week, day of the year, or othervariable circumstances specific to the individual driver at a particulartime. Further, the vehicle operator may have a reaction profile (e.g.,personalized driving attributes) that affects the timing of the warning.For example, human driving characteristics (e.g., historically recordeddriver characteristics, from probe points or otherwise) may indicateother factors that influence the warning distance. Specifically,understanding that other drivers, having similar personalcharacteristics to the individual driver, have historically made drivingmistakes, may influence the timing for warning the individual driver ofan upcoming roadway condition.

Therefore, systems, methods, and apparatuses are provided fordetermining an optimal warning distance for an individual vehicleoperator or vehicle navigation system. In particular, the embodimentsdescribed herein provide systems, methods, and apparatuses for providinga vehicle operator or vehicle navigation system with a personalizedwarning at an optimal distance from a roadway condition to adjust thespeed of the vehicle.

In certain embodiments, the systems, methods, and apparatuses comprise aserver and/or processor for receiving vehicle information and vehicleoperator information for analysis. The vehicle information may includethe speed of the vehicle, the identification of the vehicle, the traveldirection of the vehicle, and time stamp data. The vehicle operatorinformation may include personal attributes of the operator (e.g.,health, age, operator reaction time, driving characteristics). Based onthe vehicle location and heading, the database provides attributesregarding upcoming roadway segments (e.g., curves in the road, weather,traffic incidents). Further, the database may be augmented with recordedhistorical data of vehicle operators having similar characteristics andoperating a vehicle at the same roadway under similar roadway conditions(e.g., time of day, weather, construction, traffic). An optimal warningdistance may be calculated and provided to the vehicle based upon theupcoming traffic condition and the operator's reaction profile. Theembodiments are described in further detail herein.

Definitions

As used herein, a “vehicle operator” may refer to a driver of a vehicleor a passenger in an autonomous vehicle or highly assisted drivingvehicle.

As used herein, a “roadway condition” may refer to any scenario along aroadway wherein a vehicle must adjust its velocity (e.g., slow down orstop) in order to safely traverse the condition or stop before reachingthe condition. For example, a roadway condition may be an upcoming curvein the roadway requiring the vehicle's speed to be reduced from 60 km/hrto 40 km/hr in order to take the curve in the roadway safely. Otherroadway conditions affecting an adjustment in the traffic speed include,but are not limited to, weather conditions (e.g., rain, snow, ice),traffic incidents, construction, or lane closures.

As used herein, a “roadway” may refer to any traveling lane or pathwaythat may be capable of being monitored for traffic congestion/incidentdetection, or may become capable of being monitored for trafficcongestion/incident detection in the future (e.g., a highway, citystreet, bus route, train route, walking/biking pathway, waterway).

As used herein, a “reaction profile” may refer to the personalizedcharacteristics of a driver that used to determining the operator'spersonalized braking distance (D_(PR)). The factors or variablesinfluencing an operator's reaction profile may include: (1) anoperator's reaction time (T_(PR)), (2) an operator's age, (3) anoperator's health, (4) an operator's driving experience and accidenthistory, (5) an operator's experience or knowledge traveling on theroadway at issue, (6) an operator's experience or knowledge operatingthe vehicle at issue, (7) an operator's employer's driving profilepreferences, (8) an operator's insurance provider's driving profilepreferences, and/or (9) an operator's personal driving preferences. Incertain embodiments, the reaction profile comprises a combination ofmore than one factor or variable. In some embodiments, the combinationof factors may be weighted, wherein one factor is given more weight thanother factors in determining the appropriate, personalized brakingdistance.

As used herein, a “reaction time” may refer to the time it takes fromwhen an operator is notified or alerted of a roadway condition beforereacting to the notification or alert and adjusting the speed ordirection of the vehicle (T_(PR)). This reaction time includes the timethe operator needs to perceive the issue before reacting. For example, adriver of a vehicle may be alerted of that the vehicle speed needs todecrease from 60 km/hr to 40 km/hr. The driver may take 2.5 seconds tocomprehend the notification before taking any action to adjust the speedof the vehicle. The 2.5 seconds is the driver's reaction time.

As used herein, an “autonomous vehicle” may refer to a self-driving ordriverless mode in which no passengers are required to be on board tooperate the vehicle. An autonomous vehicle may be referred to as a robotvehicle or an automated vehicle. The autonomous vehicle may includepassengers, but no driver is necessary. These autonomous vehicles maypark themselves or move cargo between locations without a humanoperator. Autonomous vehicles may include multiple modes and transitionbetween the modes.

As described herein, a “highly assisted driving (HAD) vehicle” may referto a vehicle that does not completely replace the human operator.Instead, in a highly assisted driving mode, the vehicle may perform somedriving functions and the human operator may perform some drivingfunctions. Vehicles may also be driven in a manual mode in which thehuman operator exercises a degree of control over the movement of thevehicle. The vehicles may also include a completely driverless mode.Other levels of automation are possible.

Reporting/Receiving Vehicle Information and Operator Information

In order to provide an optimal warning to a vehicle operator or vehiclenavigation system of a roadway condition at an optimal time/distancefrom the condition, certain characteristics of the vehicle and theoperator are determined. In certain embodiments, vehicle information andoperator information (e.g., reaction profile of the operator) areprovided to a server and/or processor for analysis in computing theoptimal warning distance. The server and/or processor may be attached toor connected to the vehicle itself (e.g., included within a navigationdevice installed or connected to the vehicle). In some embodiments, theserver and/or processor may be part of a portable navigation device(e.g., a mobile phone) within the vehicle. In some embodiments, theserver and/or processor are located remote from the vehicle, and thevehicle and operator information are provided to the server and/orprocessor over a connected network between the vehicle and server.

The vehicle information may include an identification of the vehicle,geographic location of the vehicle (e.g., GPS), speed of the vehicle,and/or heading or travel direction of the vehicle. The operatorinformation may include an identification of the operator (e.g., driver)and/or a reaction profile of the operator (described in greater detailbelow).

Determining Roadway Conditions

Based on the location of the vehicle and the direction of travel of thevehicle, a roadway condition may be identified. The roadway conditionmay be identified by retrieving traffic or roadway attributes (e.g.,curves in the road) from a map database for the identified roadway inthe direction of travel of the vehicle. Weather information may also beretrieved from the map database. The information may include real-timetraffic data and weather collected by a navigation service provider.Alternatively, the traffic information or roadway attributes may be ahistorical database of driver speed information for a particular roadsegment at a particular time of day, day of week, or day of year. Thehistorical database may also take into account similar weatherconditions at a similar time of day, day of week, or day of year. Insome embodiments, the traffic/road attribute information may be acombination of both real-time and historical traffic information.

The information from the map database may identify a roadway conditionrequiring the vehicle to adjust its speed within a defineddistance/timeframe. As discussed in greater detail below, the distancein which the vehicle adjusts (e.g., reduces) its speed may be based on abraking distance of the vehicle and a reaction profile of the operator(e.g., reaction time).

Determining Optimal Warning Distance

In order for a vehicle to slow down or stop before reaching a roadwaycondition, the vehicle may be alerted at least a certain distance inadvance of the roadway condition. The optimal warning distance to adjustthe speed of the vehicle may be based on the vehicle's braking distanceas well as the personal characteristics or attributes of the vehicleoperator. In certain embodiments, an optimal warning distance (D_(TOT))is determined based on a combination of the vehicle's braking distance(D_(BR)) and a braking distance associated with the operator's reactionprofile (D_(PR)).D _(TOT) =D _(BR) +D _(PR)  (1)

The reaction profile factors potentially influencing to the operator'sbraking distance (D_(PR)) are described in greater detail below.

Determining Vehicle Braking Distance

In certain embodiments, the vehicle braking distance (D_(BR)) may be:(1) a predetermined fixed distance, (2) a calculated distance based onphysics principles, (3) a vehicle queue length from the roadwaycondition, (4) an average braking distance based on historical probedata, or (5) a weighted combination thereof. In one particularembodiment, the vehicle braking distance is a combination of an averagebraking distance observed from historical probe data and a vehicle queuelength from the roadway condition.

The determination of an appropriate braking distance may also rely onthe type of roadway condition (e.g., weather, road construction, roadsurface, road geometry), and the type of vehicle being operated (howquickly can the vehicle slow down).

In one embodiment, the vehicle braking distance is fixed(D_(BR)=D_(CONST)). For a particular roadway condition, an alert isdetermined at a fixed location upstream of the roadway condition. Forexample, a roadway may have a curve in the road requiring vehicleoperators to slow down from an average speed of 60 km/hr to a speed of40 km/hr. A safe, fixed braking distance may be determined to be 200meters.

In another embodiment, the braking distance is calculated based onphysics principles that take into account a vehicle's current speed(V₀), the vehicle's target speed (V₁), and the vehicle's deceleration(a). Presuming the vehicle is moving with a constant speed V₀, thevehicle is warned that it has to slow down to a speed V₁ because ofcoming change in speed limit, or a coming curve, etc. Presuming thevehicle operator brakes with constant deceleration a (e.g.,approximately 3.4 m/s²), the braking distance (D_(BR)) is calculated as:

$\begin{matrix}{D_{BR} = \frac{V_{0}^{2} - V_{1}^{2}}{2a}} & (2)\end{matrix}$

In another embodiment, the braking distance is determined based on thevehicle queue length from the roadway condition. In other words, thebraking distance from a roadway condition may be increased based on atraffic build-up. The queue length may be determined from historicaltraffic data (e.g., the average traffic build-up at a particular time ofday, day of week, or day of year) or real-time traffic data (e.g.,traffic data collected from a traffic service provider). For example,the roadway condition may be ice or snow, and the braking distance ofthe vehicle may be 200 meters to slow down from the vehicle's initialspeed to safely navigate over the ice/snow. If there is real-timereported traffic build-up or queue of vehicles that stretches for 100meters from the roadway condition, the braking distance of the vehiclemay be increased based on that queue to a distance of, for example, 300meters from the roadway condition.

In yet another embodiment, the braking distance is determined fromhistorical probe data. Historical probe data may include data collectedfrom a plurality of vehicles traveling on a particular roadway segmentat a particular time of day, day of week, or day of year, and undercertain weather and roadway conditions. In other words, probe data mayinclude a collection of data points for a number of vehicles, eachvehicle providing information regarding its speed, heading, location,timestamp, etc. The vehicle data may be obtained from a single sensorsuch as GPS or a combination of sensors such as GPS, accelerometer, andgyrometer sensors within the vehicle. The vehicle probe data may betransmitted to an external server/processor and stored in a mapdatabase. The database may be combined with weather and/or roadcondition data associated with each particular time stamp.

In certain embodiments, a navigation service provider may providehistorical probe data identifying the average vehicle speed at variouslocations of a road segment. The probe data may be used to identifywhere to alert a vehicle to adjust its speed based on when vehicleshistorically adjusted speed in advance of a roadway condition. In otherwords, based on the historical speed profile, a vehicle operator warningmay be determined where the average or median speed begins decreasing.In certain embodiments, the probe data may include additionalinformation associated with the historical data that may affect theaverage vehicle speed or timing of a vehicle speed adjustment such asthe weather conditions (e.g., rain, sunshine, ice/snow), road conditions(e.g., construction, bumpy roads, smooth resurfaced roads, width of thelanes), or the traffic conditions (e.g., congestion, open roads, trafficincidents). In some embodiments, the optimal warning distance may beincreased or decreased based on the variations in the weather, road, ortraffic conditions of the historical probe data and the current weather,road, or traffic conditions being monitored. In other embodiments, onlythe probe data that similarly matches the current weather, road, and/ortraffic conditions is used in determining the appropriate optimalwarning distance.

For example, a speed profile from historical probe data may have beendeveloped along a road segment linking one highway to another highwayvia a curved ramp. Vehicles travel on highways at roughly constant speedbut slow down on the connecting ramp. In this particular example,vehicles travel at constant speed of 60 km/hr and then slow down to 40km/hr over 200 meters, before speeding back up to 55 km/hr in another200 meters.

In another example, a speed profile from historical probe data may bedeveloped along a road segment with a peaked bridge that frequently icesover in winter when the temperature is below 0° C. and the humidity ishigh (e.g., greater than 75% relative humidity). Historical data showsthat drivers are able to navigate the hazard by slowing to 20 km/hrbefore the crest of the bridge. The data also shows that drivers that donot slow down to 20 km/hr are unable to maintain control of the vehicleon the decent. Therefore, providing an alert at an optimal warningdistance before the crest of the bridge may assist those drivers who maybe unaware of the roadway condition (such as a driver who has neverdriven on the roadway segment before, or a younger, inexperienced driverwho may not think it is necessary to slow down to 20 km/hr).

In yet another example, a blind mountain curve with an immediate stop atthe end requires a quick reaction time to stop when other vehicles arealso stopped at the end. A clear flashing light marks the dangerouscurve but distances are hard to judge for certain drivers. Accidentsoccur with drivers having slow reaction times or drivers withinexperience with driving at night or when visibility is poor. In thisexample, the mountain frequently fogs in under certain weatherconditions. Additionally, rain can make the road slippery. Historicaldata, driver characteristics, weather, time of day, and vehiclecharacteristics may play a part in determining the correct warningdistance for these road and weather conditions.

In certain embodiments, the vehicle's speed (V₀) may not be known. Insuch cases, the vehicle may have an approximated initial speed equal tothe average or median speed of historical probe data at the particularroadway location of the vehicle. In such embodiments, the vehicle'sbraking distance may be correlated with the approximated speed of thevehicle.

In certain embodiments, the vehicle's braking distance is a determinedbased on a combination of both historical probe data and physicsprinciples. For example, historical probe data may be used to determinethe initial (V₀) and final target (V₁) speeds of the vehicle along aroad segment, and physics principles could be used to calculate thevehicle braking distance from those speeds using equation (2).

Determining Operator's Reaction Profile

In order to provide a personalized alert to an individual vehicleoperator or vehicle navigation system of a roadway condition at anoptimal time, a reaction profile of the vehicle operator is determinedfor a personalized braking distance (D_(PR)). The reaction profile maycomprise characteristics such as an operator's driving preferences foradjusting speed and/or the operator's reaction time (i.e., the timeneeded to perceive an alert to brake and to react by braking).Specifically, a vehicle operator's preference or comfort level for howquickly the vehicle's speed is adjusted may be a factor to consider forwhen the vehicle receives an alert to adjust speed. For example, in thecase of an autonomous driving vehicle, the vehicle operator may not bemanually controlling the rate of acceleration or deceleration of thevehicle. Therefore, the operator's preference for when the vehiclereceives an alert to adjust its speed may depend on the operator'sdriving preferences (e.g., aggressive or conservative speedadjustments).

In certain embodiments, a reaction profile or reaction profilecharacteristics may be provided to a vehicle processor/server from aconnected network, wherein reaction profile characteristics areprogrammed prior to operating the vehicle. In other embodiments, thevehicle operator may “upload” the operator's reaction profile (or atleast certain reaction profile characteristics) upon entering thevehicle. The entering of the reaction profile characteristics may beassociated with a programmed reaction profile stored by an apparatuscarried by the operator (e.g., RFID device, a key fob, or mobile phone).In certain embodiments, a vehicle operator could transport personalizedreaction profile information regardless of the vehicle being operated.In other words, the operator could enter a new vehicle and uploadreaction profile data of the operator from other driving experiences orpreferences.

In certain embodiments, the reaction profile for determining thepersonalized braking distance (D_(PR)) may include one or more of thefollowing factors: (1) an operator's age, (2) an operator's health, (3)an operator's driving experience and accident history, (4) an operator'sexperience or knowledge traveling on the roadway at issue, (5) anoperator's experience or knowledge operating the vehicle at issue, (6)an operator's employer's driving profile preferences, (7) an operator'sinsurance provider's driving profile preferences, (8) an operator'spersonal driving preferences, and/or (9) an operator's reaction time(T_(PR)). In certain embodiments, the reaction profile comprises acombination of more than one factor or variable. In some embodiments,the combination of factors may be weighted, wherein one factor is givenmore weight than other factors in determining the appropriate,personalized braking distance.

For instance, operator's braking distance (D_(PR)) may be defined as:D _(PR) =a*V ₀ T _(PR) +b*X+c*Y  (3)wherein:

-   -   V₀ is the vehicle's initial velocity;    -   T_(PR) is the operator's reaction time;    -   X and Y refer to additional personalized characteristic factors        affecting braking distance (additional characteristics are        available, but not referenced in the equation for simplicity);        and    -   a, b, and c refer to weights attributed to each factor, wherein        0≤a, b, c≤1, and a+b+c=1.

In some embodiments, X and/or Y may be negative values, wherein theoperator's braking distance may be reduced based on the personalizedcharacteristics of the operator.

Referring to the personalized characteristic factors above, the age ofthe vehicle operator may influence the reaction profile, wherein ayounger driver may prefer to adjust speed of the vehicle more quicklythan an older driver.

Health characteristics may include whether or not the operator has anyheart conditions, hearing impairments, etc. that may affect how thevehicle speed is adjusted and how the operator is alerted to the roadwaycondition to adjust vehicle speed.

Driving experience or accident history of the operator may dictate thetiming of the alert, wherein a driver with more accidents may need anearlier warning (and larger braking distance) to adjust speed of thevehicle.

The operator's knowledge of roadway may also influence optimal warningdistance, wherein an operator with no knowledge or experience driving ona particular roadway may correspond with an increase in the operator'sbraking distance.

Similarly, an operator's knowledge of vehicle being driven may correlatewith the optimal warning distance, wherein an operator's lack ofknowledge (e.g., a rental car) may require an increase in the optimalbraking distance.

In some embodiments, an employer (e.g., a trucking company or deliverycompany) or insurance company may require its employees or vehicleoperators to drive under specific safety standards, wherein the optimalbraking distance and timing of the alert is influenced by the employer'sor insurance company's driving guidelines.

An operator's reaction time (T_(PR)) may also be correlated into areaction distance based on the vehicle's velocity at the time. In someembodiments, with reference to equation (3) above, a=1, b=0, and c=0. Inother words, the operator's braking distance is a function of theoperator's reaction time:D _(PR) =V ₀ T _(PR)  (4)

In certain embodiments, the operator's reaction time (T_(PR)) may bebased on: (1) a predetermined fixed value, (2) a manually entered valueby the operator, (3) a computed reaction time, (4) a variable orhistorical reaction time, or (5) a combination thereof. In someembodiments, the operator's reaction time may be a combination ofvarious factors. For example, the reaction time may be based on thecomputed reaction time of the operator and historical reaction time dataof the operator over time. Such a combination of factors may be aweighted average, wherein more weight is given to one factor over otherfactors.

Predetermined Fixed Reaction Time

In one embodiment, the vehicle operator reaction time is a predeterminedfixed reaction time requiring no input from the vehicle operator. Thefixed reaction time may serve as a default reaction time. For example, avehicle operator may have a default fixed reaction time of 2.5 seconds,wherein the default value presumes that it will take the operator about2.5 seconds after receiving an alert to comprehend and take action toadjust the speed of the vehicle.

In some embodiments, the fixed reaction time is determined based on anaverage reaction time compiled among a plurality of vehicle operators. Adatabase may compile a plurality of vehicle operator reaction timesbased on any number of factors (such as those described in greaterdetail below) to determine a default reaction time. For example, thedefault reaction time may be the average reaction time of a number ofreaction times manually entered by vehicle operators.

Manually Entered or Uploaded Reaction Time

In another embodiment, the vehicle operator may manually enter areaction time. For example, upon entering the vehicle, prior to driving,an operator may enter into a vehicle computer system a preferredreaction time (e.g., 2.5 seconds) for the time the operator believes itwill take to hear or see an alert to adjust speed before beginning toadjust speed. The vehicle operator may manually enter a reaction timeeach time the operator enters and starts the vehicle. In otherembodiments, after entering a reaction time once, the time serves as adefault reaction time until manually adjusted by the operator.

In certain embodiments, as mentioned above, the vehicle operator may“upload” a preferred reaction time upon entering the vehicle. Thisentering of a reaction time may be associated with a reaction timestored by an apparatus carried by the operator (e.g., RFID device, a keyfob, or mobile phone). In certain embodiments, a vehicle operator couldtransport personalized reaction time information regardless of thevehicle being operated. In other words, the operator could enter a newvehicle and upload reaction time data of the operator from other drivingexperiences or preferences. This may be a bootstrap for learning thereaction time by the vehicle operating system.

Operator or Vehicle Computed Reaction Time

In certain embodiments, a reaction time is computed through completionof a series of visual and/or audio tests by the operator. Upon enteringthe vehicle, prior to driving, an operator may be presented with aseries of tests on the vehicle computer system. The tests may includeclicking a button or tapping on a display screen in response to seeing achange in color on the display screen or hearing an audio signal. Theoperator's average reaction time in response to the series of tests maybe correlated with a reaction time while operating the vehicle. Forexample, an operator may have an average test response time to theseries of visual tests of 0.3 seconds. Such a response time maycorrelate to a reaction time while driving of, for example, 1.5 seconds.

The vehicle operator may conduct the series of tests for a computedreaction time each time the operator enters and starts the vehicle. Thetests may also be conducted at time intervals throughout the trip,wherein, if the vehicle is in motion, the tests may be limited to audiosignals only. In other embodiments, after computing a reaction timeonce, the computed time may serve as a default reaction time untiltested again by the operator.

In some embodiments, the vehicle operator reaction time may be computedby a vehicle server/navigation system and/or an external server after aperiod of use of the vehicle. The vehicle navigation system may be incommunication with a map database (whether stored within the vehiclesystem or outside the vehicle system and connected through a network).Correlations may be made between the operation of the vehicle androadway conditions registered in the map database. Specifically, thevehicle or external system may calculate an operator's reaction timesbased on when the operator made an adjustment in speed for a specificroadway condition. Such reaction times may take into account anoperator's driving preferences (i.e., whether to slow down quickly orslow down gradually).

Variable and Historical Reactions Time

In certain embodiments, an operator reaction time may be variable. Anoperator's reaction time may be different at different times of the day(e.g., 12:30 p.m. versus 3:30 a.m.), different days of the week (e.g.,workday versus weekend), or different days of the year (e.g., workdayversus holiday). Therefore, an operator's reaction time may be adjustedbased upon the time of day. In some embodiments, a fixed or defaultreaction time is provided for various time windows of the day, week, oryear. In other embodiments, adjustments may be made to a manuallyentered or computed reaction time based upon differences in the time thereaction time was manually entered or computed and the present time theoperator is traveling in the vehicle. For example, a computed reactiontime at 12:30 p.m. of 2 seconds may be adjusted to 3 seconds when theoperator is traveling in the vehicle at 3 a.m. The adjustment may becomputed by a server stored within the vehicle or an external server incommunication with the vehicle.

In some embodiments, the operator reaction time may be based onhistorical data. For example, the reaction time may be set based onpreviously determined reaction times at similar times of day, days ofthe week, or days of the year. Reaction time may also be varied based onthe operator's vehicle location and historical reaction times recordedand stored near the vehicle location.

Determined reaction times, such as manually entered or computed(operator or vehicle), may be stored in a vehicle or external databasefor future reference. In some embodiments, upon entering the vehicle, anoperator's reaction time may be determined based on the operator'spreviously entered or computed reaction time saved in the reaction timedatabase. The reaction time may then vary throughout the operation ofthe vehicle based on a change in the time of day and a correlatedreaction time in the database, or a change in the location of thevehicle and a correlated reaction time in the database.

Weighted Factors

As mentioned above, determination of the reaction time may be weightedbased on a plurality of factors. For example, a vehicle or externaldatabase may store historical reaction time data based on manual orcomputed information. This information may be combined with anoperator's current manual entry or computation of a new reaction timeupon entering the vehicle. In some embodiments, more weight will begiven to a current computed reaction time over historical data. In otherembodiments, more weight will be given to historical data over a manual,subjectively entered reaction time by a vehicle operator. In someembodiments, the weighting of data may be adjusted as the vehicle isoperated and additional driving data is collected. In some embodiments,more weight may be given for the newly collected driving data.

Reporting the Optimal Warning Distance

As discussed above, the optimal warning distance (D_(TOT)) may bedetermined and reported based on a combination of the vehicle's brakingdistance (D_(BR)) and a braking distance associated with the operator'sreaction profile (D_(PR)). When the vehicle's geographic locationcoincides with the optimal warning distance from the roadway condition,the operator or an operating system (e.g., navigation system) of thevehicle may be alerted of the roadway condition.

The alert may be transmitted from an external processor/server to thevehicle's navigation system, or may be generated by the vehicle'sinternal processor/server associated with the navigation system. In someembodiments, the alert is relayed to the vehicle operator as an audiomessage through the vehicle's speaker system or a navigation device'sspeaker located within the vehicle. In other embodiments, the alert isrelayed to the vehicle operator as a visual message displayed on avehicle navigation system screen or on a navigation device screen. Inyet other embodiments, the alert is relayed to the vehicle operator asboth an audio and a visual message.

The alert may provide the type of roadway condition ahead, as well asthe course of action to take to avoid a traffic incident. For example,the alert may provide instructions that there is ice on the roadwayahead (e.g., “Caution, ice ahead”), and suggest the operator/vehiclereduce speed to avoid a potential accident (e.g., “Adjust speed to 20km/hr”).

The warning distance may be calculated and/or reported by aprocessor/server within or connected to the vehicle, or the warningdistance may be calculated and/or reported by a processor/server remotefrom the vehicle, wherein the alert is transmitted over a connectednetwork to the vehicle. In some embodiments, the alert may betransmitted at a determined distance before the vehicle reaches theoptimal warning distance based on a known or perceived delay in thetransmission of the signal to the vehicle such that the vehicle willreceive the alert transmission at the optimal warning distance.

Examples

A non-limiting example for determining an optimal warning distance(D_(TOT)) is depicted in FIG. 1. In FIG. 1, the roadway condition is anicy patch of roadway 112 ahead of the vehicle 110. The vehicle istraveling at an initial speed (V₀) of 60 km/hr. The operator has areaction time (T_(PR)) of 2.5 seconds. A processor has determined thatthe icy road segment 112 can be safely traversed at a speed (V₁) of 40km/hr. In this particular example, the optimal warning distance(D_(TOT)) is calculated using a combination of physics principles forthe vehicle's braking distance and the reaction time of the operator:

$\begin{matrix}{D_{TOT} = {\frac{V_{0}^{2} - V_{1}^{2}}{2a} + {V_{0}T_{PR}}}} & (5)\end{matrix}$

The vehicle may decelerate safely at a rate of 3.4 m/s². From thesevariables, the braking distance of the vehicle (D_(BR)) is 22.7 m andthe operator's reaction distance (D_(PR)) is 41.7 m. The total distance(D_(TOT)), i.e., the optimal warning distance, is 64.4 m. At the optimalwarning distance, the vehicle operator receives an alert 114.

A second non-limiting example determines an optimal warning distance(D_(TOT)) based on a fixed or constant braking distance(D_(BR)=D_(CONST)), and a personal attribute of the vehicle operator. Inthis example, the vehicle is an autonomous vehicle and requires nooperator reaction time to adjust the speed of the vehicle. Combiningequations (1) and (3), the optimal warning distance is calculated as:D _(TOT) =D _(BR) +a*V ₀ T _(PR) +b*X+c*Y,  (6)wherein D_(BR)=D_(CONST), a=0, b=1, and c=0, resulting in:D _(TOT) =D _(CONST) +X.  (7)

In this example, the personal attribute X is associated with theoperator's age and health. That is, the vehicle operator is 75 years oldand has a heart condition. Based on these attributes, theprocessor/server may determine that the operator would prefer additionaltime to adjust the speed of the vehicle (i.e., a longer overall brakingtime). Therefore, X is a positive braking distance value that increasesthe overall braking distance. As such, the server alerts the vehiclefarther upstream of the roadway condition to adjust the speed of thevehicle at a comfortable level for the operator.

Flowchart Embodiment

FIG. 2 illustrates an example flowchart for determining an optimalwarning distance. The process of the flowchart may be performed by avehicle navigation device and its processor and/or a server and itsprocessor. Alternatively, another device may be configured to performone or more of the following acts. Additional, fewer, or different actsmay be included.

At act S101, a processor and/or server receives a geographic location ofa vehicle. In some embodiments, the processor/server also receives adirection of travel of the vehicle. As noted, the processor/server maybe included within or attached to the vehicle. In other embodiments, theprocessor and/or server may be located remotely from the vehicle and incommunication with the vehicle through a network.

At act S103, a reaction profile of the operator of the vehicle isreceived. The reaction profile may include the operator's age, theoperator's health, the operator's preferred driving style, the reactiontime of the operator, or a combination thereof. In some embodiments, thereaction profile of the operator is a weighted combination of thereaction time of the operator and at least one of the operator's age,the operator's health, or the operator's preferred driving style.

At act S105, based on the geographic location (and direction of travelof the vehicle), a roadway condition is determined. The determinationmay be made based on a correlation of the location and direction oftravel and a map database of traffic information, which may be providedby a traffic service provider.

At act S107, the optimal warning distance is calculated based on thebraking distance of the vehicle and the personal attributes of theoperator. The braking distance of the vehicle may be a predeterminedfixed distance, a calculated distance based on physics principles, adistance based on historical probe data, or a weighted combinationthereof.

At act S109, the operator (and/or a device associated with the operator)and/or navigation system of the vehicle are alerted to the roadwaycondition at or in proximity of the optimal warning distance. In someembodiments, the alert is an audio message or a video message relayedthrough the vehicle's speakers or navigation system display screen or anavigation device's speaker or display screen. The alert may provide thevehicle operator with the type of roadway condition ahead, as well asthe course of action to take to avoid a traffic incident (e.g.,“Caution, ice ahead. Reduce speed to 20 km/hr,” or “Caution, road curveahead. Adjust speed to 40 km/hr,” or “Caution, traffic congestion ahead.Reduce speed to 0 km/hr.”) In some embodiments, in an autonomousvehicle, the operator may not need to be alerted. In such cases, onlythe navigation system is alerted to the roadway condition.

Navigation Devices and Systems

As discussed above, determining an optimal warning distance for avehicle operator may be performed by a vehicle navigation device and itsprocessor and/or a server and its processor. FIG. 3 illustrates oneembodiment of a traffic navigation system 120. The system 120 mayinclude a vehicle 110, a map developer system 121, a vehicle navigationdevice 122, a workstation 128, and a network 127. Additional, different,or fewer components may be provided.

The navigation device 122 may be a personal navigation device (“PND”), aportable navigation device smart phone, a mobile phone, a personaldigital assistant (“PDA”), watch, camera, a tablet computer, a notebookcomputer, and/or any other known or later developed mobile device orpersonal computer that may perform navigation-related functions (such asdigital routing or map display). Non-limiting embodiments of navigationdevices may also include RDS devices, HD radio devices, mobile phonedevices, or car navigation devices such as Garmin or TomTom.

The map developer system 121 includes a server 125, a map database 123,and a reaction profile database 129. The developer system 121 mayinclude computer systems and networks of a system operator such as HERE,NAVTEQ, or Nokia Corporation. The map database 123 may be configured tostore traffic data at specified geographic locations of interest,wherein the data has been collected from historical probe data orreal-time traffic data, or the data has been developed from predictivetraffic data. The map database 123 may include may include historicalprobe data collected from a plurality of vehicles traveling on aparticular roadway segment at a particular time of day, day of week, orday of year, and under certain weather and roadway conditions. In otherwords, the database 123 may include historical probe data for a numberof vehicles, each vehicle providing information regarding its speed,heading, location, timestamp, etc. Data from within the map database 123may be used to determine a vehicle braking distance from data such as adetermined or predicted vehicle queue length from a roadway conditionand/or an average braking distance based on the historical probe data.

In certain embodiments, the map database 123 may include node datarecords, road segment or link data records, points of interest (POI)data records, and other data records. More, fewer or different datarecords can be provided. In one embodiment, the other data recordsinclude cartographic (“carto”) data records, routing data, and maneuverdata. One or more portions, components, areas, layers, features, text,and/or symbols of the POI or event data can be stored in, linked to,and/or associated with one or more of these data records. For example,one or more portions of the POI, event data, or recorded routeinformation can be matched with respective map or geographic records viaposition or GPS data associations (such as using known or future mapmatching or geo-coding techniques), for example.

In exemplary embodiments, the road segment data records are links orsegments representing roads, streets, or paths, as can be used in thecalculated route or recorded route information for determination of oneor more personalized routes, according to exemplary embodiments. Thenode data records are end points corresponding to the respective linksor segments of the road segment data records. The road link data recordsand the node data records represent a road network, such as used byvehicles, cars, and/or other entities. Alternatively, the map database123 can contain path segment and node data records or other data thatrepresent pedestrian paths or areas in addition to or instead of thevehicle road record data, for example.

The road/link segments and nodes can be associated with attributes, suchas geographic coordinates, street names, address ranges, speed limits,turn restrictions at intersections, and other navigation relatedattributes, as well as POIs, such as gasoline stations, hotels,restaurants, museums, stadiums, offices, automobile dealerships, autorepair shops, buildings, stores, parks, etc. The map database 123 caninclude data about the POIs and their respective locations in the POIdata records. The map database 123 can also include data about places,such as cities, towns, or other communities, and other geographicfeatures, such as bodies of water, mountain ranges, etc. Such place orfeature data can be part of the POI data or can be associated with POIsor POI data records (such as a data point used for displaying orrepresenting a position of a city). In addition, the map database 123can include event data (e.g., traffic incidents, constructions,scheduled events, unscheduled events, etc.) associated with the POI datarecords or other records of the map database 123.

The map database 123 can be maintained by the content provider (e.g., amap developer or traffic service provider) in association with theservices platform. By way of example, the map developer can collectgeographic data to generate and enhance the map database 123. There canbe different ways used by the map developer to collect data. These wayscan include obtaining data from other sources, such as municipalities orrespective geographic authorities. In addition, the map developer canemploy field personnel to travel by vehicle along roads throughout thegeographic region to observe features and/or record information aboutthem, for example. Also, remote sensing, such as aerial or satellitephotography, can be used.

The map database 123 can be a master map database stored in a formatthat facilitates updating, maintenance, and development. For example,the master map database or data in the master map database can be in anOracle spatial format or other spatial format, such as for developmentor production purposes. The Oracle spatial format ordevelopment/production database can be compiled into a delivery format,such as a geographic data files (GDF) format. The data in the productionand/or delivery formats can be compiled or further compiled to form mapdatabase products or databases, which can be used in end user navigationdevices or systems.

For example, geographic data is compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as an end user device, for example. The navigation-relatedfunctions can correspond to vehicle navigation, pedestrian navigation,or other types of navigation. The compilation to produce the end userdatabases can be performed by a party or entity separate from the mapdeveloper. For example, a customer of the map developer, such as anavigation device developer or other end user device developer, canperform compilation on a received map database in a delivery format toproduce one or more compiled navigation databases.

As mentioned above, the server side map database can be a master mapdatabase, but in alternate embodiments, the client side map database canrepresent a compiled navigation database that can be used in or with enduser devices to provide navigation and/or map-related functions. Forexample, the map database can be used with the end user device toprovide an end user with navigation features. In such a case, the mapdatabase can be downloaded or stored on the end user device, such as inapplications, or the end user device can access the map database througha wireless or wired connection (such as via a server and/or thecommunication network), for example.

In certain embodiments, the reaction profile database 129 may beseparate or combined with the map database 123. The reaction profiledatabase 129 may be configured to store reaction times for variousvehicle operators. For each stored reaction time, affiliated informationmay be linked, such as operator identification, time stamp, a geographiclocation.

The developer system 121, the workstation 128, and the navigation device122 are coupled with the network 127. The phrase “coupled with” isdefined to mean directly connected to or indirectly connected throughone or more intermediate components. Such intermediate components mayinclude hardware and/or software-based components.

The workstation 128 may be a general purpose computer includingprogramming specialized for providing input to the server 125. Forexample, the workstation 128 may provide settings for the server 125.The settings may include a value for the predetermined interval that theserver 125 requests the navigation device 122 to relay currentgeographic locations. The workstation 128 may be used to enter dataindicative of GPS accuracy to the map database 123. The workstation 128may also be used to enter data to the reaction profile database 129indicative of the vehicle operator's reaction time or other personalizedattributes (e.g., an operator's age, an operator's health, an operator'sdriving experience and accident history, an operator's knowledge of thegeographic location, an operator's experience operating the vehicle, anoperator's employer's driving profile preferences, an operator'sinsurance provider's driving profile preferences, an operator's personaldriving preferences, or a combination thereof). The workstation 128 mayinclude at least a memory, a processor, and a communication interface.

FIG. 4 illustrates an exemplary navigation device 122 of the system ofFIG. 3. The navigation device 122 includes a processor 200, a memory204, an input device 203, a communication interface 205, positioncircuitry 207, and a display 211. Additional, different, or fewercomponents are possible for the navigation device 122.

The processor 200 may be configured to receive data indicative of thelocation of the navigation device 122 from the position circuitry 207.The positioning circuitry 207, which is an example of a positioningsystem, is configured to determine a geographic position of thenavigation device 122. The positioning system may also include areceiver and correlation chip to obtain a GPS signal. The positioningcircuitry may include an identifier of a model of the positioningcircuitry 207. The processor 200 may access the identifier and query adatabase or a website to retrieve the accuracy of the positioningcircuitry 207 based on the identifier. The positioning circuitry 207 mayinclude a memory or setting indicative of the accuracy of thepositioning circuitry.

FIG. 5 illustrates an exemplary server 125 of the system of FIG. 3. Theserver 125 includes a processor 300, a communication interface 305, anda memory 301. The server 125 may be coupled to a map database 123, areaction profile database 129 and/or a workstation 128. The workstation128 may be used as an input device for the server 125. In addition, thecommunication interface 305 is an input device for the server 125. Incertain embodiments, the communication interface 305 may receive dataindicative of use inputs made via the workstation 128 or the navigationdevice 122.

The navigation device processor 200 and/or the server processor 300 mayinclude a general processor, digital signal processor, an applicationspecific integrated circuit (ASIC), field programmable gate array(FPGA), analog circuit, digital circuit, combinations thereof, or othernow known or later developed processor. The navigation device processor200 and/or the server processor 300 may be a single device orcombinations of devices, such as associated with a network, distributedprocessing, or cloud computing.

The navigation device processor 200 and/or the server processor 300 mayalso be configured to cause an apparatus to at least perform at leastone of the methods described above. For example, the navigation deviceprocessor 200 and/or server processor 300 may be configured to performthe process of: (1) receive a geographic location and direction oftravel of a vehicle; (2) receive a reaction profile of an operator ofthe vehicle; (3) determine a roadway condition in the direction oftravel of the vehicle based on the geographic location of the vehicle;(4) calculate an optimal warning distance from the roadway conditionbased on a braking distance of the vehicle and the reaction profile ofthe operator; and (5) alert the operator or a navigation system of thevehicle to the roadway condition when the vehicle is located at theoptimal warning distance.

The memory 204 and/or memory 301 may be a volatile memory or anon-volatile memory. The memory 204 and/or memory 301 may include one ormore of a read only memory (ROM), random access memory (RAM), a flashmemory, an electronic erasable program read only memory (EEPROM), orother type of memory. The memory 204 and/or memory 301 may be removablefrom the navigation device 122, such as a secure digital (SD) memorycard.

The communication interface 205 and/or communication interface 305 mayinclude any operable connection. An operable connection may be one inwhich signals, physical communications, and/or logical communicationsmay be sent and/or received. An operable connection may include aphysical interface, an electrical interface, and/or a data interface.The communication interface 205 and/or communication interface 305provides for wireless and/or wired communications in any now known orlater developed format.

In certain embodiments, determination and retrieval of a traffic mapimage on the navigation device may be used to provide functions for anautonomous vehicle or highly assisted driving (HAD) vehicle. Thenavigation device 122 or another computer system in communication withthe navigation device 122 may include instructions for routing oroperating the autonomous or highly assisted driving vehicle. Anestimated travel time may be calculated based on the traffic map dataand a route may be chosen based on the estimate travel time. Thecomputing system may generate driving commands for steering the vehicle,shifting gears, increasing and decreasing the throttle, and braking. Thecomputing system may generate auxiliary commands for controlling theheadlights, turn signals, windshield wipers, defrost, or other auxiliaryfunctions not directly related to the movement of the vehicle.

The autonomous vehicle may include sensors for identifying thesurrounding and location of the car. The sensors may include GPS, lightdetection and ranging (LIDAR), radar, and cameras for computer vision.Proximity sensors may aid in parking the vehicle. The proximity sensorsmay detect the curb or adjacent vehicles. The autonomous vehicle mayoptically track and follow lane markings or guide markings on the road.

In the above described embodiments, the network 127 may include wirednetworks, wireless networks, or combinations thereof. The wirelessnetwork may be a cellular telephone network, an 802.11, 802.16, 802.20,or WiMax network. Further, the network 127 may be a public network, suchas the Internet, a private network, such as an intranet, or combinationsthereof, and may utilize a variety of networking protocols now availableor later developed including, but not limited to TCP/IP based networkingprotocols.

While the non-transitory computer-readable medium is described to be asingle medium, the term “computer-readable medium” includes a singlemedium or multiple media, such as a centralized or distributed database,and/or associated caches and servers that store one or more sets ofinstructions. The term “computer-readable medium” shall also include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by a processor or that cause a computersystem to perform any one or more of the methods or operations disclosedherein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

As used in this application, the term “circuitry” or “circuit” refers toall of the following: (a) hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) and (b) tocombinations of circuits and software (and/or firmware), such as (asapplicable): (i) to a combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone or server, to perform various functions) and (c) tocircuits, such as a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation, evenif the software or firmware is not physically present.

This definition of “circuitry” applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) or portionof a processor and its (or their) accompanying software and/or firmware.The term “circuitry” would also cover, for example and if applicable tothe particular claim element, a baseband integrated circuit orapplications processor integrated circuit for a mobile phone or asimilar integrated circuit in server, a cellular network device, orother network device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read only memory or arandom access memory or both. The essential elements of a computer are aprocessor for performing instructions and one or more memory devices forstoring instructions and data. Generally, a computer also includes, orbe operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio player, a Global Positioning System (GPS) receiver, to namejust a few. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., E PROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, are apparent to those of skill in the artupon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention. The claims should not be read as limited to thedescribed order or elements unless stated to that effect. Therefore, allembodiments that come within the scope and spirit of the followingclaims and equivalents thereto are claimed as the invention.

We claim:
 1. A method comprising: receiving a geographic location of anautonomous vehicle; accessing a road attribute based on the geographiclocation; identifying a personalized braking distance for a passenger ofthe autonomous vehicle, wherein the passenger is not a driver of thevehicle; accessing a passenger profile for the passenger in response tothe passenger entering the autonomous vehicle, wherein the profileincludes personalized braking distance for a comfortable rate of brakingfor the passenger based on a personal attribute of the passenger;calculating an optimal warning distance based on the accessed roadattribute and the personalized braking distance of the passenger; andproviding a message for the autonomous vehicle in response to theoptimal warning distance.
 2. The method of claim 1, wherein the messageincludes a command for the autonomous vehicle.
 3. The method of claim 2,wherein the command is a driving command configured to operate theautonomous vehicle.
 4. The method of claim 3, wherein the drivingcommand steers the autonomous vehicle, shifts a gear of the autonomousvehicle, or adjusts a throttle of the autonomous vehicle.
 5. The methodof claim 3, wherein the driving command applies a brake of theautonomous vehicle.
 6. The method of claim 1, wherein the messageincludes an alert.
 7. The method of claim 1, wherein the message is anaudio message or a video message relayed to the passenger of theautonomous vehicle.
 8. The method of claim 1, wherein the message isprovided at the optimal warning distance to a roadway condition.
 9. Anapparatus comprising: at least one processor; and at least one memoryincluding computer program code for one or more programs; the at leastone memory and the computer program code configured to, with the atleast one processor, cause the apparatus to at least perform: receivinga geographic location of an autonomous vehicle; accessing a roadattribute based on the geographic location; identifying a personalizedbraking distance for a passenger of the autonomous vehicle; accessing apassenger profile for the passenger, wherein the profile includespersonalized braking distance for a comfortable rate of braking for thepassenger based on a personal attribute of the passenger; calculating anoptimal warning distance based on the accessed road attribute and thepersonalized braking distance of the passenger; and providing a messagefor the autonomous vehicle in response to the optimal warning distance.10. The apparatus of claim 9, wherein the message includes a command forthe autonomous vehicle.
 11. The apparatus of claim 10, wherein thecommand is a driving command configured to operate the autonomousvehicle.
 12. The apparatus of claim 11, wherein the driving commandsteers the autonomous vehicle, shifts a gear of the autonomous vehicle,or adjusts a throttle of the autonomous vehicle.
 13. The apparatus ofclaim 11, wherein the driving command applies a brake of the autonomousvehicle.
 14. The apparatus of claim 9, wherein the message includes analert.
 15. The apparatus of claim 14, wherein the message is an audiomessage or a video message relayed to the passenger of the autonomousvehicle.
 16. The apparatus of claim 15, wherein the message is providedat the optimal warning distance to a roadway condition.
 17. Anon-transitory computer readable medium containing program instructionsfor causing a computer to perform a method of: receiving a geographiclocation of an autonomous vehicle; accessing a road attribute based onthe geographic location; identifying a personalized braking distance fora passenger of the autonomous vehicle; accessing a passenger profile forthe passenger, wherein the profile includes personalized brakingdistance for a comfortable rate of braking for the passenger based on apersonal attribute of the passenger; calculating an optimal warningdistance based on the accessed road attribute and the personalizedbraking distance of the passenger; and providing a message for theautonomous vehicle in response to the optimal warning distance.
 18. Thenon-transitory computer readable medium of claim 17, wherein the messageincludes a driving command that steers the autonomous vehicle, shifts agear of the autonomous vehicle, or adjusts a throttle of the autonomousvehicle.
 19. The non-transitory computer readable medium of claim 17,wherein the message includes an alert including audio or video relayedto the passenger of the autonomous vehicle at the optimal warningdistance to a roadway condition.